CNN based aircraft dynamic monitoring through remote sensing images

In military fields, it is essential to monitor the dynamic of aircrafts through remote sensing images. Due to lack of automated assistive analysis methods of recent works, we propose a novel method for automatically monitoring the dynamic of aircrafts through remote sensing images in this paper. The method consists of two phases: (i) establish a priori model of aircrafts in airports and learn a Convolutional Neural Networks (CNN) classifier that identifies the state of aircrafts, and (ii) predict the states of aircrafts in the new images. The proposed method was tested on the remote sensing images of two typical airports. Experimental results show that the method is able to monitor the dynamic of aircrafts with high accuracy. We conclude that the method can report the states of aircrafts in airports correctly.